Audiences for content blocks, such as broadcast media or online streamed media, including commercial advertising, are typically measured via single-source data panels consisting of individuals or households recording content exposure over time. In one typical system, panel members record daily or weekly journals identifying watched content. However, if a panel member forgets that they watched an item of content, measurements may be inaccurate. Another system provides panel members with portable devices or meters that detect audio watermarks embedded in content. While an improvement on journal-based systems, such systems require panel members to continuously wear or carry the meter, as failure to do so results in missed measurements.
Other audience measurement systems may be built into smart televisions, cable demodulators, digital video recorders, or set-top boxes to record identifications of content displayed. These systems may accurately record content that has been shown, on a household basis, but may not be able to distinguish individuals within the household. Such systems may also be used for personalization, such as for recommending video-on-demand content based on previously viewed content. In demographically diverse households or households with members with different interests, this may result in poor recommendations. For example, a typical family may include one member who watches sports, another member who watches reality television programming, and still another who watches children's programming. Without the ability to distinguish between viewers, a recommendation system may suggest children's content to the sports viewer or vice versa. While some systems allow multiple user profiles, users must remember to diligently select their own profile each time they watch an item of content. Additionally, the system may not be able to distinguish between a single user and multiple users, such as a family watching a movie together, while one user is logged in. Content chosen by a group of viewers is frequently a compromise selection based on their combined interests, and may not be content that would be watched by any viewer alone. Accordingly, when the logged-in user subsequently seeks content to view alone, the system may make inaccurate recommendations.